The invention relates generally to image segmentation. More specifically, embodiments of the invention relate to methods and systems which provide computer-based ground glass nodule segmentation using Markov random field analysis and intensity model adaptation for fast, objective and consistent volume measure for lung cancer diagnosis.
Lung cancer remains a leading cause of cancer death in both women and men in the USA. More people die of lung cancer than of colon, breast, and prostate cancers. Assuming that intervention at early stages leads to higher survival rates, it is a major public health directive to improve the survival rate and to reduce the mortality of lung cancer through detection and intervention at an earlier and potentially more curable stage.
Computed tomography (CT) is considered to be the most accurate imaging modality available for early detection and diagnosis of lung cancer. CT uses special equipment to obtain multiple cross-sectional images of the organs and tissues of the chest, and produces images that are far more detailed than a conventional chest x-ray. CT images can show different types of tissue, including the lungs, heart, bones, soft tissues, muscle and blood vessels, and can be acquired within a single breath hold with a 1 to 3 mm axial collimation. CT scans today use a method called helical CT that captures images of the chest from many angles, and with the aid of a computer, processes the images to create cross-sectional axial, coronal or sagittal images or “slices” of the area of interest. Each image can then be printed out or examined on a computer monitor. CT offers high resolution and fast acquisition times and has resulted in the opportunity to detect small lung nodules in these thin image slices which may represent lung cancers at earlier and potentially more curable stages.
The disadvantage is the examination of hundreds of images. Hundreds of CT images taken per examination must be evaluated by a radiologist. Examinations in the traditional sense of looking at each image in the axial mode are difficult to interpret and lead to a high false-negative rate for detecting small nodules. This results in the potential to miss small nodules, and therefore miss a cancer.
Computer analysis can assist the radiologist in the treatment of lung cancer. For the detection of small lung nodules, a primary metric is size for estimating growth rate. Since cancer is growth, this is one of the most direct methods of indicating cancerous nodules. A second is shape. Determining growth rates requires time and repeated measurements. By observing the size, shape and the appearance of the nodule, a determination of whether a nodule is malignant or benign can be made.
Ground glass nodules (GGNs) are often associated with malignant cancer, and quantitative measure of GGNs is of great importance in cancer diagnosis. GGNs appear as partial opacities in CT and magnetic resonance (MR) lung images and comprise two types, the pure, and the sub-solid or mixed.
GGNs have proved especially problematic in lung cancer diagnosis, as despite frequently being malignant they characteristically have extremely slow growth rates. This is further magnified by the small size of many of these lesions now being routinely detected following the introduction of multi-slice CT scanners capable of acquiring contiguous, high resolution, 1 to 1.25 mm image sections throughout the thorax within a single breath hold.
A number of computer-aided methods and systems for the automated detection of small nodules from CT chest images have been developed over the years and comprise density-based and model-based approaches. Considering the fact that lung nodules have relatively higher densities than those of lung parenchyma, density-based detection methods employ techniques such as multiple thresholding, region-growing, locally adaptive thresholding in combination with region-growing, and fuzzy clustering to identify nodule candidates in the lungs. False-positive results can then be reduced from the detected nodule candidates by employing a priori knowledge of small lung nodules.
For the model-based detection approaches, the relatively compact shape of a small lung nodule is taken into account while establishing the models to identify nodules in the lungs. Techniques such as N-Quoit filter, template-matching, object-based deformation, and the anatomy-based generic model have been proposed to identify sphere-shaped small nodules in the lungs. Other attempts include automated detection of lung nodules by analysis of curved surface morphology and improvement of the nodule detection by subtracting bronchovascular structures from the lung images. Due to the relatively small size of the existing CT lung nodule databases and the various CT imaging acquisition protocols, it is hard to compare the detection performance among the developed algorithms.
Using the computer-aided diagnosis techniques, the nodule is extracted from the CT images. The difficulty is determining which voxels, or which parts of which voxels belong to a nodule. Once an accurate representation is obtained, measuring size and shape parameters is simplified.
To perform a growth rate measurement, segmentation is performed. Two dimensional methods observe a single image slice and calculate the area of a nodule present on that slice. Three dimensional methods observe the entire volume.
Image segmentation identifies homogeneous regions in an image. The homogeneity can be based on one or more properties such as texture, color, distribution of the densities of the image elements, motion field, etc. The result of the segmentation is either an image of labels identifying each homogeneous region, or a set of contours which describe the region boundaries.
Image segmentation can be performed on 2-dimensional images, sequences of 2-D images, 3-dimensional volumetric imagery or sequences of the latter. Some image segmentation research has focused on 2-D images. If the data is defined on 3-D lattices, such as obtained from series of cross-sectional CT or MRI images, then each image slice is segmented individually.
There exist various techniques for image segmentation. Most methods have been extended from 2-D to 3-D. Random field-based modeling has been extensively used due to its suitability both for analyzing and synthesizing images. The result of the texture segmentation task is very difficult to assess automatically, and typically requires an observer to judge the quality of the segmentation.
Although segmentation of solid nodules can be used clinically to determine volume doubling times quantitatively, reliable methods for segmentation of GGNs have yet to be introduced. Achieving consistent segmentation of GGNs has proven problematic most often due to indistinct boundaries and interobserver variability. What is desired is a computer-based method for obtaining fast, reproducible quantitative measurements of these lesions.